As of now our team sees a few potential risks/blockers for achieving MVP. Firstly is the design of the case for our embedded system. We are currently iterating on different approaches for attaching our system to the handle of the racket without impeding on the user’s experience. This may or may not take longer than 3 weeks to get in an acceptable range. Secondly is designing the ML pipeline for this system. We have done substantial outside research to give us confidence that our design can succeed. However, we will undoubtedly need to make changes to some parameters as is reasonable with our own hardware and data flow. Lastly and newly, we are a bit concerned with how often we may need to be calibrating our IMU to have it work consistently for as long as possible.
In order to mitigate risk 1: we will be constantly iterating our design to minimize user impact. We will be reaching out to collegiate tennis players that we know in order to get feedback on our ideas and implementation as we go.
In order to mitigate risk 2: we will be working on creating a baseline pipeline that works structurally the same but with less expected accuracy. We want to uncover unexpected issues as quickly as possible to make needed changes. We will additionally be doing testing and verification of our data flow to ensure data integrity as it goes through our ML pipeline. This will be pivotal in ensuring accurate results when testing the ML.
In order to mitigate risk 3: we will be working with filters and doing our best to calibrate the IMU to the fullest before making decisions as to integrate more sensors or change up our approach.
We have made successful transition from collecting data to the computer via bluetooth to actually being able to use the IOS application to collect data. This is great for the demo and great for our future progress. We have also made improvements to the IMU gyroscope and accelerometer readings by applying filtering to the sample data.
